Optimal Parameter Combination Research Articles

Performance analysis of powder-assisted micro-drilling operation using micro-EDM

Miniaturization is governed by the precise and accurate micro-machining operations of various applications. Micro-EDM has emerged as a feasible method for attaining microscale high-precision drilling. The present paper aims to conduct an experimental investigation for drilling micro-holes on copper using the Alumina (Al2O3) powder mixed Micro-EDM technique. Process parameters, including voltage, feed rate, and powder concentration, are varied in experimentation to evaluate their impact on response measures like MRR, TWR, taper angle, and aspect ratio. ANOVA determines the individual contribution of process parameters on each response measure. Voltage is the primary factor influencing MRR (93.5%), TWR (58.24%), and aspect ratio (67.3%), whereas the interaction effect of voltage with powder concentration primarily affects the taper angle (32.36%). Furthermore, the GRA method has optimized the multiple parameters for different responses. The optimum combination of parameters was obtained at a voltage of 125 V, powder concentration of 0.8 g/L, and tool travel speed of 9 µm/s at which better responses are received. The results obtained from FESEM analyses indicate that utilizing alumina (Al2O3) Nano-powder in the dielectric fluid leads to improved effectiveness and reduces the formation of micro-cracks, and micro-voids. Furthermore, the formation of recast layer is reduced by 36.49% as compared to without powder mixed EDM oil during micro-drilling operations.

Grindability Evaluation of Ultrasonic Assisted Grinding of Silicon Nitride Ceramic Using Minimum Quantity Lubrication Based SiO2 Nanofluid

Minimum quantity Lubrication (MQL) is a sustainable lubrication system that is famous in many machining systems. It involve the spray of an infinitesimal amount of mist-like lubricants during machining processes. The MQL system is affirmed to exhibit an excellent machining performance, and it is highly economical. The nanofluids are understood to exhibit excellent lubricity and heat evacuation capability, compared to pure oil-based MQL system. Studies have shown that the surface quality and amount of energy expended in the grinding operations can be reduced considerably due to the positive effect of these nanofluids. This work presents an experimental study on the tribological performance of SiO2 nanofluid during grinding of Si3N4 ceramic. The effect different grinding modes and lubrication systems during the grinding operation was also analyzed. Different concentrations of the SiO2 nanofluid was manufactured using canola, corn and sunflower oils. The quantitative evaluation of the grinding process was done based on the amount of grinding forces, specific grinding energy, frictional coefficient, and surface integrity. It was found that the canola oil exhibits optimal lubrication performance compared to corn oil, sunflower oil, and traditional lubrication systems. Additionally, the introduction of ultrasonic vibrations with the SiO2 nanofluid in MQL system was found to reduce the specific grinding energy, normal grinding forces, tangential grinding forces, and surface roughness by 65%, 57%, 65%, and 18% respectively. Finally, regression analysis was used to obtain an optimum parameter combinations. The observations from this work will aid the smooth transition towards ecofriendly and sustainable machining of engineering ceramics.

Novel entropy generation and optimization for hybrid nanofluid flow via ribbed surface parabolic trough collector

This research paper focuses on the optimization of parameters for deformative receiver tubes to achieve prime choice of design for efficient energy absorption. The motivation of the current study lies in addressing the critical need for optimizing parameters of deformative receiver tube to obtain optimal design to enhance energy absorption efficiency in Parabolic Trough Solar Collectors (PTSC). Considering the diverse criteria involved in the design process, the application of a Multi Criteria Decision Making (MCDM) approach proves beneficial. The primary objective of this study is to identify the optimal combination of parameters (Reynolds number (Re), Relative V-rib height (Rh/ Dt), Relative streamwise pitch (PStm/Rh), and Relative spanwise pitch ratio (PSpn/Rh)) of the deformative receiver tube of PTSC) employing the multiobjective optimization based on ratio analysis (MULTIMOORA) method. Additionally, a novel Pythagorean fuzzy sets (PFSs) entropy-based approach is proposed for quantifying the weights of the criteria. The feasibility and practicality of the novel PFSs entropy and MULTIMOORA method are demonstrated through the application of the Technique for Order of Preference by Similarity to Ideal Solution method (TOPSIS), confirming the attainment of Re = 50000, Rh/ Dt=0.0135, Pstm/ Rh=56.67 and Pspn/ Rh=11.11, as optimal alternative parameters. This research offers valuable insights into optimizing deformative receiver tube parameters to improve thermal performance which benefiting researchers in the concerned field.

Design and Performance Analysis of a Sunflower Cutting Table Based on the Principle of Manual Disk Pick-Up

To solve the problems associated with the poor harvesting ability of existing sunflower harvester cutting tables, and high seed drop rates, we designed a sunflower cutting table that can greatly improve the operational performance of sunflower combine harvesters. In this paper, we introduce the structure and principle of the whole machine and select the key parameters of the cutting table with the goal of adapting to a variety of planting modes in Xinjiang, China. Since the harvesting of sunflower in the wrong row easily causes the sunflower stalks to break, ADAMS (Version: 2020) simulation experiments were carried out to investigate the effects of the forward speed of the machine, the height of the sunflower insertion disk, and the angle of inclination of the harvest divider on the offset angle of the sunflower. With the goal of reducing the offset angle of the stalks in the forward direction of the harvest divider and reducing the size of the cutting table, the harvest divider inclination angle was chosen to be 45°; by using Design-Expert V13.0.15 software, a three-factor, three-level field test was carried out to determine the optimal parameter combinations that resulted in the minimum seed loss rate and the maximum success rate of the disk picking. Moreover, a validation test was conducted. The results show that when the forward speed is set to 0.62 m/s, the lifting speed of the pick-up disk device is set to 0.42 m/s, and the height of the inserted disk is set to 1000 mm, the relative errors between the theoretical values of the disk-picking success rate of the cutting table and the seed loss rate and the field test values are 6.5% and 1.3%, respectively. The results of the present study can provide a reference for improving the performance of sunflower harvester cutting tables and for the mechanical harvesting of inserted disk sunflowers.

A Combined Paddy Field Inter-Row Weeding Wheel Based on Display Dynamics Simulation Increasing Weed Mortality

Weeds compete with rice for sunlight and nutrients and are prone to harboring pathogens, leading to reduced rice yields. Addressing the issues of low weeding efficiency and weed mortality rates in existing inter-row weeding devices, the study proposes the design of a combination paddy field inter-row weeding wheel. The device’s operation process is theoretically analyzed based on the weed control requirements in the northeastern region of China, leading to the determination of specific structural parameters. This research conducted experiments on the mechanical properties of weed cutting to obtain geometric parameters for paddy field weeds. It was found that the range for the cutting gap of the dynamic–fixed blade is between 0.6 mm to 1.4 mm and the cutting angle is between 5° to 15°, resulting in the lowest peak cutting force for weeds. Using LS-DYNA R12.0.0 dynamic simulation software, a fluid–structure interaction (FSI) model of the weeding wheel–water–soil system was established. By employing the central composite experimental design principle and considering the soil stir rate and coupling stress as indicators, the optimal structural parameter combination for the device is obtained: a dynamic–fixed blade cutting gap of 1.4 mm, a cutting angle of 10.95°, and a dynamic blade install angle of −3.44°. Field experiments demonstrated that the device achieved an average weeding rate of 89.7% and an average seedling damage rate of 1.9%, indicating excellent performance. This study contributes to improving weed mortality rates and provides valuable guidance for inter-row mechanical weeding technology.

Brain Age Prediction Using Multi-Hop Graph Attention Combined with Convolutional Neural Network.

Convolutional neural networks (CNNs) have been used widely to predict biological brain age based on brain magnetic resonance (MR) images. However, CNNs focus mainly on spatially local features and their aggregates and barely on the connective information between distant regions. To overcome this issue, we propose a novel multi-hop graph attention (MGA) module that exploits both the local and global connections of image features when combined with CNNs. After insertion between convolutional layers, MGA first converts the convolution-derived feature map into graph-structured data by using patch embedding and embedding-distance-based scoring. Multi-hop connections between the graph nodes are modeled by using the Markov chain process. After performing multi-hop graph attention, MGA re-converts the graph into an updated feature map and transfers it to the next convolutional layer. We combined the MGA module with sSE (spatial squeeze and excitation)-ResNet18 for our final prediction model (MGA-sSE-ResNet18) and performed various hyperparameter evaluations to identify the optimal parameter combinations. With 2788 three-dimensional T1-weighted MR images of healthy subjects, we verified the effectiveness of MGA-sSE-ResNet18 with comparisons to four established, general-purpose CNNs and two representative brain age prediction models. The proposed model yielded an optimal performance with a mean absolute error of 2.822 years and Pearson's correlation coefficient (PCC) of 0.968, demonstrating the potential of the MGA module to improve the accuracy of brain age prediction.

Application of Machine Learning Methods for Process Optimization in Electronic Packaging Processes

Epoxy resins are commonly used as encapsulation materials in electronic packaging processes. Fluctuations in the materials lead to both changes in processability and to varying quality. Ideally, these variations should be identified, and measures taken as quickly as possible to reduce scrap parts and thus costs. A promising optimization approach for encapsulation processes are machine learning models, which have already shown good results in quality predictions, especially for injection molding. Subsequent quality measurements are avoided with good quality prediction models. With this type of models, not only predictions can be made, but also optimal parameter combinations can be found. In this paper, models for predicting quality criteria warpage and residual enthalpy of the epoxy molding compound were set up, trained and validated. Time series of in-situ sensors were used, from which relevant features were extracted and which, together with machine parameters, provide a dataset for prediction. The most promising prediction models are random forest regression and gradient boosting regression. T hey predict warpage with an accuracy of 90% to 91% and the residual enthalpy with an accuracy of 95%. Subsequently, optimization models of the machine parameters were set up. All relevant target variables were considered in a cost function, through the minimization of which an optimal parameter set was found. The gradient boosted tree and Bayesian optimization were determined to be the most promising models, as they lead to the lowest values of the respective cost function.

Design and Experiment of Oil-Electric Hybrid Air-Suction Sorghum Plot Seeder

To tackle the issues of low seeding accuracy and seed injury caused by the seeders utilized at a small scale and in the plot seeding of sorghum in mountainous or hilly regions, this study presents the design of an oil–electric hybrid air suction sorghum plot seeder. The main working parts of the seeder are described, and the performance of the seed-mixing device is simulated using EDEM software. An oil–electric hybrid drive mode is used to provide power for operation and to the seed-metering device and fan. Additionally, a sowing control and monitoring system is designed using a single-chip microcomputer controller to ensure uniform plant spacing at different forward speeds. A multi-factor experiment is conducted using the central synthesis method to determine the optimal operating parameters of the seed-metering device through bench tests. The results show that a profile hole diameter of 2.5 mm on the seed tray, a negative-pressure chamber vacuum of 8.0 kPa, and a seed-metering device speed of 28 r/min result in a 95.95% pass rate, 0.5% missing rate, and 3.55% reseeding rate. The deviation between the experimental and analytical results that validate the optimum parameters is kept within acceptable limits. Field tests are conducted at different forward speeds using the optimum parameter combinations, and a comparison is made with the widely used duckbill planter. The results show pass, missing, and reseeding rates of 94.41%, 2.3%, and 3.29%, respectively. The missing monitoring error is less than 7.19%. All of the indices of the oil–electric hybrid air suction sorghum plot seeder are superior to those of the duckbill planter; thus, it fulfills the agronomic requirements for seeding a sorghum plot.

Optimizing the Preparation Process of Refined Asphalt Based on the Response Surface and Preparation of Needle Coke.

Refined asphalt was prepared by solvent extraction sedimentation based on the response surface design, using washing oil and kerosene as solvents and the coal tar pitch as raw materials. The mathematical models of the refined asphalt yield, quinoline insoluble (QI) content, ash content, solvent-to-oil ratio, aromatic-to-aliphatic hydrocarbon ratio, extraction temperature, and sedimentation time were proposed, analyzing the influence of each factor and their interactions on the response values. Therefore, the optimal combination of preparation process parameters and better operation window was obtained by optimizing the experiment. Meanwhile, refined asphalt with high QI content and low QI content was selected as raw material, and the needle coke was prepared through the process of carbonization and calcination. The influence of QI content on the composition and the structure of green coke and needle coke was investigated by X-ray diffraction (XRD), Raman spectra, and polarizing microscopy characterizations. The results showed that the solvent-to-oil ratio is 1.2, aromatic-to-aliphatic hydrocarbon ratio is 1.1, sedimentation time is 2 h, and extraction temperature is 110 °C, resulting in the yield of refined asphalt being 76%, QI content being less than 0.1%, and ash content being less than 0.05%, which meets the requirement of the high-quality needle coke. Otherwise, refined asphalt with lower QI content easily generates a mesophase with more fibers and a large structure in the thermal conversion process, and the corresponding green coke and needle coke have a relatively regular carbon microcrystalline structure.

Investigation on the optimal ultra-precision machining of nitriding mold steel by in-situ laser assisted diamond cutting

S136 (STAVAX ESR) mold steel is an advanced material for the optical mold industry. The precisely machined surface quality is essential for guaranteeing the excellent performance of molds. Diamond cutting is a high-efficiency machining technique in optical mold manufacturing. However, the chemical wear of diamond tool is a huge challenge in precisely machining steel mold due to the reaction between atoms C and Fe. In this study, the hybrid machining technique which combined the in-situ laser assisted diamond cutting (LADC) and plasma nitriding was used to machine S136 mold steel. Furthermore, the impact of the various machining conditions on the machined workpiece surface roughness Sa was comprehensively investigated by the Taguchi method (TM) and Response surface methodology (RSM). The experimental results of material characterization indicated that a 90 μm nitride layer was generated on the workpiece surface by plasma nitriding. Analysis of variance (ANOVA) results demonstrated that the feed rate and laser power had an important impact on the machined surface roughness Sa. According to the RSM, a precise mathematical model was developed which is explored by experimental investigations. The results showed that the optimal machining parameter combination by RSM generated the better surface quality of 6.393 nm in in-situ LADC of nitriding steel. This study provides a reference for mold steel ultra-precision machining technique and parameter optimization method for surface integrity.

Research on a Vibrationally Tuned Directional Seed Supply Method Based on ADAMS-EDEM Coupling and the Optimization of System Parameters

We have combined the theory of bulk dynamics and the agronomic requirements of precision sowing with the aim of resolving the technical problems of poor seed mobility and the difficulty in controlling suction posture, which leads to an increase in the leakage rate and a reduction in seed qualification index scores. In this study, a vibrationally tuned directional seed supply method and system are proposed. We carried out a force analysis of seeds, constructed kinematic equations for seeds and seed boxes to specify the state of the seed motion, and determined the structural parameters and the range of structural parameters that affect the seed suction posture. In addition, we coupled the ADAMS-EDEM simulation of the motion process of the seed and seed boxes and analyzed the vibrational tuning process of the seeds and the angle of inclination of the bottom surface of the seed box. The speed of the eccentric wheel and the eccentric distance were used as test factors. Three-factor and three-level Box–Behnken central combination testing with a single-grain rate, multiple-grain rate, and cavity rate were used as response indicators. Mathematical models were obtained between the experimental factors and the response indicators. Multi-objective optimization of mathematical regression models was carried out with Design-Expert 10.0.4 software. The optimal parameter combination obtained was a tilt angle of 14.27°, an eccentric wheel speed of 4.48 rad/s, and an eccentricity of 1.94 mm. The rate of single grains was 90.75%, the rate of multiple grains was 3.63%, and the rate of cavities was 5.62%. In bench performance tests, using an angle of inclination of 14°, the speed of the eccentric wheel was 4.50 rad/s and the eccentricity was 2 mm. The mean value of the single-grain rate was 89.28%, the mean value of the multiple-grain rate was 3.89%, and the mean value of the cavity rate was 6.83%. The test error was within permissible limits, and reliable results were achieved for parameter optimization. The results met the technical requirements for precision sowing. The results of the study can provide academic references for theoretical research on the methodology of posturing and directional seed supply. They can also provide ideas for the design and development of seed supply systems for precision sowing machinery.

Design and decoupling analysis of Thermal–Electric energy comprehensive utilization scheme based on “diamond” active cooling thermal protection system for hypersonic vehicle

High–temperature aerodynamic heat threatens the safe cruise of hypersonic vehicles, but it also contains huge energy utilization potential. In this study, the thermal–electric energy comprehensive utilization scheme was proposed to combine the energy conversion technology (ECT) with active cooling thermal protection system (ACTPS). The mass flow rate (M) varied from 0.008 kg s−1 to 0.2 kg s−1 for each channel with the inlet fluid temperature being set to 253 K. It was investigated that the leading edge of the “Enhanced” model forms longitudinal swirls. The inner and outer wall temperatures of the “Enhanced” model were lower than 275 K and 650 K, respectively, at mass flow rate of 0.076 kg s−1. Moreover, the thermoelectric performance of five different parameters (Bi2Te3 height (L1), SiGe height (L2), width for unit (H), inner wall temperature (Tin) and heat power (QT)) was explored. L1 = 1.0 mm, L2 = 0.5 mm and H=1.5 mm were selected as the optimal parameters combination for engineering applications. Within the range of QT from 0.15 W to 0.9 W, the variations range of rated current (Irc), energy conversion efficiency (ηe) and specific power (Ps) for the optimal parameters were 140 mA–460 mA, 0.69 %–2.61 % and 0.016 W g−1–0.373 W g−1, respectively.

The milling parameters of mechanical parts are optimized by NC machining technology

In the field of mechanical manufacturing, CNC machining technology plays an important role in improving the precision and efficiency of part processing. However, how to further improve the effect of NC machining by optimizing milling parameters is still a key problem. The aim of this study is to optimize CNC milling parameters through systematic research and experiments to improve the machining efficiency and quality of mechanical parts. By adjusting key parameters such as tool speed, feed speed, and removal rate cutting depth, the influence of these parameters on the milling process was systematically studied using advanced CNC machining equipment. Through the collection and analysis of experimental data, the mathematical model is established, and the optimization algorithm is applied to find the best combination of milling parameters. The experimental results show that under the optimal combination of parameters, the surface quality of parts can be significantly improved, the machining time can be reduced, and the tool wear can be reduced. This research successfully realizes the optimization of milling parameters of mechanical parts by CNC machining technology and provides an effective solution for improving machining efficiency and reducing costs. This not only has guiding significance for the application of CNC machining technology but also has important promotion value in actual production.

Establishment and Parameter Calibration of a Simulation Model of Coated Cotton Seeds and Soil

Precision seeding technology is an important component of agricultural mechanization production. The precise regulation of seed movement behavior is the core of precision sowing technology and the key to improving the quality of single seed precision sowing. To accurately obtain the interaction law between seeds and soil after touching the soil, it is necessary to conduct comprehensive physical experiments to determine the simulation parameters of the seed and soil. This article takes coated cotton seeds as the research object, and the basic physical parameters of coated cotton seeds are measured through biological experiments. Based on the Hertz–Mindlin with bonding V2 contact model, a simulation model of compression between coated cotton seeds and soil is established. Using peak compression force as the response value, a combination of physical experiments and simulation simulations was used to calibrate the simulation parameters of the simulation mode of coated cotton seeds and soil. Through PB testing, it was found that four factors have a significant impact on the peak compressive force, and the parameter range was obtained. The Poisson’s ratio of coated cotton seeds was 0.14–0.26. The static friction coefficient between coated cotton seeds and steel plate was 0.38–0.58. The static friction coefficient between soil and soil was 0.3–1.2. The rolling friction coefficient between soil and soil was 0.1–0.6. Through response surface experiments with four factors and three levels, regression models were established between various factors and response values, and the optimal combination of simulation parameters was determined: the Poisson’s ratio of coated cotton seeds was 0.21; the static friction coefficient between coated cotton seed and steel plate was 0.47; the static friction coefficient between soil and soil was 0.34; and the rolling friction coefficient between soil and soil was 0.59. Based on the optimal parameter combination, the simulation of compression between coated cotton seeds and soil was continued, and the variation law of soil particle bonding bonds at different positions of coated cotton seeds during the compression process was obtained. This study provides a basis for exploring the interaction mechanism between the trencher seed soil of precision seeders and optimizing the design of critical components of cotton precision seeders.

Experimental and numerical study on flow characteristics of dual‐cavity die based on multi‐objective optimization

AbstractSlot die coating is a common method of manufacturing electrodes for lithium‐ion batteries. In this paper, a multi‐objective optimization research on the geometry of dual‐cavity die is investigated based on the numerical simulation of lithium battery anode slurry flow combined with the surface response optimization method. First, we use the Plackett–Burman test method to screen the geometric parameters significantly affecting the optimization objective. Then, based on the Box–Behnken response surface analysis method, we determine the optimal combination of parameters. The results show that the inner cavity radius and slot gap are the main structural variables affecting exit‐velocity uniformity and die deformation. The slot gap and slot length mainly affect the inlet pressure. The coefficient of deviation of exit velocity uniformity and average inlet pressure of the optimized die structure decreased by 51.3% and 7.6%, respectively. This research provides theoretical guidance for the design of slot coating dies.Highlights Screening significant geometric parameters using Plackett–Burman test methodology. Box–Behnken response surface analysis to determine optimal parameters. Velocity uniformity and inlet pressure increased by 51.3% and 7.6%, respectively.

Effect of laser micromachining crater-array–multi-grooves on the bonding strength and failure mode of aluminum alloy adhesive joints

The strength of aluminium (Al) alloy adhesive joints is essential for aerospace, ship and automotive applications. This study proposes the two-step laser processing of crater-array–multi-groove (CAMG) to maximize the shear strength of adhesive joints for 7075-T6 aluminum alloys. Firstly, the effects of CAMG feature parameters (groove pattern, groove spacing L, and groove depth Gp) on the surface roughness (Sa), shear strength (τ), and microscale failure mode were studied. The failure mode and anchoring structure reveal the influence mechanism of CAMG feature parameters on τ. Then, the parameters are optimized to obtain the best combination that maximizes τ. Finally, the advantages of CAMG were demonstrated by comparing it with a reference specimen and analyzing the surface morphology, wettability, chemical properties, and anchorage structure. The results show that for CAMG the surface had high cleanliness, wettability, and chemical modifications. The multi-scale anchoring structure formed by the grooves, craters, and micropores on the surfaces with the adhesive was sufficiently strong to allow cohesive failure (CF) to occur throughout the bonding region, maximizing τ. When the groove pattern is parallel-groove, L ≤ 100 μm, and the number of scans is N = 2–5, CAMG maximizes τ (∼29.34 MPa). To increase efficiency and save energy, the optimal combination of parameters for laser processing CAMG is parallel-groove, L = 100 μm, and N = 2. Compared with laser processing multi-grooves, CAMG improved the processing efficiency by 3.5 % and reduced the laser energy by 35.2 %. The proposed method shows great advantages in improvement of bonding strength.

Investigation of micro-electro-discharge machining process parameters during machining of M2 hardened die-steel

Micro-electro-discharge machining (micro-EDM) is a well-known micromachining method to create microstructures on hard-to-cut materials. To further exploit the capability of micro-EDM, the present research work investigates the effect of various important micro-EDM process parameters on the material removal rate (MRR), tool wear rate (TWR), and overcut (OC) during micro-cavity machining of M2-hardened die-steel. A Taguchi experimental design was used to study the effects of three process parameters namely pulse-on-time (Ton), peak current (Ip), and gap voltage (Vg). The experimental results showed that the most influencing process parameter in the present experimental investigation is peak current, Ip followed by pulse-on-time, Ton and gap voltage, Vg. Furthermore, a recent and effective method i.e. Measurement of Alternatives and Ranking according to COmpromise Solution (MARCOS) has been successfully employed to determine optimal process parametric combination for micro-EDM process to achieve maximum MRR and minimum TWR and OC during machining of micro-cavity on M2-hardened die-steel. From the present research investigation, the optimal micro-EDM process parametric combinations obtained for higher MRR, lower TWR and OC are found as 1 A of peak current, 30 μs pulse-on time and gap voltage of 60 V. Furthermore, through various scanning electron microscope micrographs of micro-cavities and micro-machined surfaces, analysis has been done to find out the micro-cavity with best geometrical accuracy and surface feature to validate the best and most ideal combinations of micro-EDM input parameters for micromachining operations. The results of this study show that micro-EDM is a promising process for machining M2-hardened die-steel. The optimization of process parameters using the MARCOS methodology can further improve the machining efficiency and accuracy of micro-EDM.

Experimental study of the effect of different 3D printing parameters on tensile strength, using artificial neural network

Numerous research studies have been conducted to optimize printing parameters using the fused deposition modeling technique (FDM) to improve mechanical properties. The large number of process parameters creates a need to search for optimal combinations of parameters to improve mechanical properties. This study examines the effects of three parameters when printing 3D with melted filament of a PLA material (Polylactic Acid) on the ultimate tensile strength of the printed parts. This search combines an experimental study of the most influential printing parameters on the tensile strength property, namely layer thickness, printing temperature, and feed rate. The experimental results are then analyzed and modeled as a linear regression model. Then develop an intelligent artificial model based on ANN (Artificial Neural Network) derived from these experimental results capable of predicting the optimal combination of parameters providing maximum tensile strength. The observed results showed that the feed rate dominates among the other variables, followed by the thickness of the layer. Also, at the level of prediction, the artificial model provides a better prediction of the tensile strength with a value of 36.1625 MPa by combining the following parameters: Feed rate: 70 mm s−1, temperature: 200 °C, and layer thickness: 0.26 mm, compared to the prediction obtained by the linear regression model. Neural networks enable more accurate optimization of 3D process parameters, leading to an overall improvement in the quality of finished products. predictive models, significantly reducing the iteration time required to obtain optimal parameters. The quality of the data used to train neural networks is crucial.

An Experimental Analysis of the Seed-Filling Mechanism of Maize-Precision Hole-Planter Clamping

Because the operating speed of current mechanical maize hole seeders is low and their ability to adapt to the seed is poor, an active clamping-type precision hole planter for corn was designed. Here, we explain its structural composition and working principle. According to the maize kernel size, the combination of hole parameters is based on the principle of virtual work on analyzing the seed extraction disc assembly’s static mechanical model. The model was imported into the ADAMS simulation for validation and the parameters and ranges affecting the seed-filling performance were identified. By further analyzing the results of the coupled ADAMS–EDEM simulation, the “arching” process of the seeds during leakage charging was revealed, and an arch-breaking method was proposed with the help of a swinging seed-collecting slider. The speed of the hole planter, the diameter of the outer edge of the gravity ring, and the angle of the block installation were used as test factors. The Box–Behnken center-combination simulation test was conducted using the sowing pass index, re-seeding index, and missed sowing index as evaluation indices. The experimental results show that the optimal parameter combination was as follows: gravity ring = 174.3 mm, stopper installation angle = 131.9°, and hole seeder speed = 85.2 rpm. At this time, the qualified seeding index was 94.53%, the multiple indices were 4.30%, and the leakage index was 1.18%. Under these conditions, the row seeding performance bench test was conducted to obtain the qualified seeding index of the hole seeder, which was 93.36%, while the multiple indices were 5.20% and the leakage index was 1.44%, which satisfied the agronomic requirements of precision seeding. This provides a theoretical reference for mechanical seeding methods for irregular seeds, as well as a basis for the research and development of maize precision sowing machinery and equipment.

Experimental optimization of machining GH4145 by atomizing discharge ablation milling

Atomizing discharge ablation milling (ADAM) technology is an efficient discharge machining technology derived from the traditional electrical discharge machining (EDM) method, which can be used to efficiently machine hard-to-machine materials such as nickel-based superalloy. In this present, the performance of machining nickel-based superalloy GH4145 by ADAM and Air near-dry EDM were compared, and the experimental results showed that the material removal rate (MRR) obtained by ADAM was nearly double that of the latter. A single-factor experiment were conducted to investigate the effect of electrode rotation speed on ADAM’s processing performance. Subsequently, an orthogonal experimental method was used to design the experiment. The signal-to-noise ratio analysis method was used to systematically study the performance characteristics of ADAM, including the influence of atomization amount, oxygen pressure, discharge current, duty ratio on MRR and tool electrode relative wear rate (TWR). The results showed that discharge current was the most influential processing parameter on MRR and TWR. Finally, the optimal combination of processing process parameters that met the requirements of various processing effect evaluation indicators were obtained and the correctness of the single objective optimization results was verified through experiments.